Outline

Organizations need to manage data across ecosystems, develop data pipelines, APIs, insight into their metadata, and try to make sure that silos and data quality issues are managed effectively.

Enter data observability platforms.

This blog post looks at what drives many organizations to adopt data observability to ensure the health of your data across systems and providers.

Although there are many important considerations when adopting a data observability solution, 7 key capabilities are reviewed to make sure they are considered before adoption.

Understanding The Drivers of Data Observability

More solutions are becoming available for organizations that want to monitor the health of their data stack proactively. As organizations become more data-drive, the ability to support data engineering teams and ensure overall better data management has become more of a priority.

Although, dashboards and metrics have been available for quite awhile, most organizations have focused on IT traffic or leveraging analytics for customer-facing applications. Generally, organizations should take advantage of the increase in data observability platform options. Many are driven to adopt solutions due to:

  • The fact that systems are complex and hard to ensure overall data monitoring across multiple data sets. With silos and more data volumes being collected daily, managing data across systems is complex and complicated at the best of times. Automating solutions helps ensure visibility across the data ecosystem and should provide insight irrespective of where data resides.

  • Cloud adoption, micro services, and the need for the ability to monitor data flows across distributed data systems to enable data reliability.

  • The need for automation. This includes the ability to automate processes and define rules for alerting to respond to issues quickly, as well as ensure that data integration processes are automated as much as possible. 

  • Security and compliance because it helps organizations comply by gaining visibility over data and detect and prevent security threats.

  • The need for better business insights. Data observability can help organizations better use their data.

Data Observability Solution Considerations

Once an organization decides they are interested in investing in data observability, they should consider the following aspects to ensure they get the most out of any data observability tool. 

  1. Data collection: This is an essential component of any data related solution. For data observability, the ability to gather data from various sources and store it in a centralized location for analysis is key. Companies need to ensure that data can be accessed from both disparate and complex data sources. Connectors do not always exist and the more complex an environment the more likely setup will be more time consuming. 

  2. Data querying and correlation: Organizations need to be able to identify patterns, relationships, and dependencies within data to make sure that issues can be identified proactively. In many cases this will involve data profiling and identifying correlations related to potential data quality challenges and inconsistencies across data sources.

  3. Alerting: Alerts may be the most important aspect of data observability. Alerts notify users of any unusual or abnormal behavior in the data and let people make informed decisions on how to improve on overall data health over time. These alerts should also be used to improve overall data quality processes over time.

  4. Root-cause analysis: Organizations need to the ability to investigate and identify the underlying cause of any issues or anomalies in the data. Making sure a solution can do this and how data is tracked within the system is key to ensuring better decision making.

  5. Anomaly detection: The ability to automatically detect and flag any unusual or abnormal data patterns is another essential part of data observability. Within a toolset, machine learning is used. to help identify the origin of the issue and allows data engineers and devops to fix the issues at the source to ensure better quality over time.

  6. Traceability: Data lineage and traceability helps an organization understand the flow of data through different systems, from origin to destination. The value of data lineage extends beyond observability and is required within any data pipeline created to ensure visibility into data, to understand overall flows and ensure relevant business outcomes. Some observability tools may flag data, but it is important to be able to identify the root source of any issue or inconsistency within the data ecosystem.

  7. Data visualization: Alerts need to visualized within a dashboard but not all dashboards are created equally. Any dashboard needs to be customized to ensure it can change and be updated as data health improves across the organization. Organizations also need to evaluate their latency requirements to identify whether they need real-time alerts to ensure visibility or can manage with less up-to-date data access.

Bottom Line

The market is investing more heaving in data observability as many organizations are not having the amount of desired success with their data projects. Observability becomes key so that companies can enhance their data quality and overall health over time.